An empirical experimentation towards predicting understandability of conceptual schemas using quality metric

Abstract

Data warehouse are used in organisations for efficient information delivery. The quality of a data warehouse is governed by the quality of conceptual, logical and physical data models. Conceptual model forms the base for design of logical/physical models. The conceptual model quality is assessed using quality metrics. The metrics for assessing the quality of conceptual schemas are based on size/structural complexity of schemas. Various statistical techniques show the existence of significant relationship between quality metrics and understanding time of conceptual models. In this paper, the authors analyse the ability of quality metrics in predicting the understandability of conceptual schemas using experimental empirical approach. Various statistical techniques are used for study and analysis. The results of empirical analysis show that few of the metrics are strong indicators for predicting the understandability of conceptual multidimensional models.